In the last 10 years social media has effectively changed the way people communicate online. Millions of blogs, or services like Twitter and Facebook enabled new ways of expression for people worldwide. Billions of messages, blog posts, pictures, videos etc are published on a daily basis. As such, it has become very important for companies to find a way to analyze this real-time stream of information.
Many companies use a social monitoring service like uberVU that offer systems to collect all mentions of a specific brand, a product, or an event. The way this usually works is by creating a persistent search, which will constantly collect and analyze all the new mentions of a specific brand, a product, or an event. However, this may require a big brand to collect and analyze potentially multiple millions of mentions on a weekly basis; particularly, if it tracks the brand, products, and/or competitors.
With the total amount of data being so great, it becomes impractical, or even impossible, to sort through and consider this monitored activity. Moreover, generating useful insights that account for this data becomes equally daunting. As such, there is a desperate need for a system to sort through large data sets from social monitoring that can analyze and detect patterns or things that are out of the ordinary.